#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2019/3/28 20:43
# @Author  : Seven
# @File    : inference.py
# @Software: PyCharm
# function : 检测车辆
import cv2
import numpy as np
import tensorflow as tf
import visualization_utils as vis_util
import label_map_util
import os

NUM_CLASSES = 90
rootdir = os.getcwd()


def detection(image_path):
    PATH_TO_CKPT = os.path.join(rootdir, "data/frozen_inference_graph.pb")
    PATH_TO_LABELS = os.path.join(rootdir, "data/mscoco_label_map.pbtxt")
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')
    label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                                use_display_name=True)
    category_index = label_map_util.create_category_index(categories)

    test_img_path = image_path
    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
            detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
            num_detections = detection_graph.get_tensor_by_name('num_detections:0')
            image_np = cv2.imread(test_img_path)
            image_np_expanded = np.expand_dims(image_np, axis=0)
            (boxes, scores, classes, num) = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections],
                feed_dict={image_tensor: image_np_expanded})
            # 获取车辆的bbox和scores
            boxes_size, scores_list = vis_util.visualize_boxes_and_labels_on_image_array(
                image_np,
                np.squeeze(boxes),
                np.squeeze(classes).astype(np.int32),
                np.squeeze(scores),
                category_index,
                use_normalized_coordinates=True,
                line_thickness=8)
        return boxes_size, scores_list


if __name__ == '__main__':
    image = 'images/3.jpg'
    detection(image)
    cv2.waitKey(0)
